用生成式AI预测油田产油量，提升决策精准度
Generative AI-driven forecasting of oil production
- 结合TimeGrad与Informer模型，处理长达四十年的多井时间序列数据
- Informer在所有站点表现最优，预测精度高于TimeGrad且更高效
- 适合能源行业从业者、油气田规划者及量化分析师参考
在石油和地热能开采以及储能技术中，对多井油藏的产油量进行预测是一项关键任务。预测准确性直接影响经济预测、油气储量估算、流体处理设施建设和能源价格波动。本文利用生成式AI方法，对跨越四个十年的四个多井站点的油水产量时间序列进行建模。目标是有效捕捉不确定性并实现精准预测，以支持油田层面的决策。采用自回归模型TimeGrad和专为长序列时间序列设计的Transformer变体Informer。两者预测结果均与真实数据高度吻合。Informer整体表现突出，在所有站点上均展现出更高的预测效率和精度。
原文摘要 · Abstract (English)
Forecasting oil production from oilfields with multiple wells is an important problem in petroleum and geothermal energy extraction, as well as energy storage technologies. The accuracy of oil forecasts is a critical determinant of economic projections, hydrocarbon reserves estimation, construction of fluid processing facilities, and energy price fluctuations. Leveraging generative AI techniques, we model time series forecasting of oil and water productions across four multi-well sites spanning four decades. Our goal is to effectively model uncertainties and make precise forecasts to inform decision-making processes at the field scale. We utilize an autoregressive model known as TimeGrad and a variant of a transformer architecture named Informer, tailored specifically for forecasting long sequence time series data. Predictions from both TimeGrad and Informer closely align with the ground truth data. The overall performance of the Informer stands out, demonstrating greater efficiency compared to TimeGrad in forecasting oil production rates across all sites.
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